High-dimensional cluster analysis with the masked EM algorithm
- Submitting institution
-
University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13613178
- Type
- D - Journal article
- DOI
-
10.1162/NECO_a_00661
- Title of journal
- Neural Computation
- Article number
- -
- First page
- 2379
- Volume
- 26
- Issue
- 11
- ISSN
- 0899-7667
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 143
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In anticipation of the new generation of large dense probes containing hundreds of channels for electrophysiology, e.g. Neuropixel probes (Jun et al., Nature 2017), this paper reports the first algorithm able to handle the huge emergent technical problem of spike sorting for these probes. Previous algorithms were unable to deal with more than 8 channels, whereas this algorithm enables scalable analysis of electrophysiological data from many neurons at high temporal resolution - vital in experimental studies concerning how neuronal activity coordinates brain function and behaviour (Goldem et al. Nature 2016, Lin et al. Neuron 2015, Watson et al., Neuron 2016).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -